CASIA OpenIR  > 毕业生  > 硕士学位论文
Alternative TitleStudy on Quasi-dense Wide Baseline Matching of Building Scene
Thesis Advisor吴毅红
Degree Grantor中国科学院研究生院
Place of Conferral中国科学院自动化研究所
Degree Discipline模式识别与智能系统
Keyword准稠密匹配 宽基线图像 最大稳定极值区域 仿射传递 自适应支持加权 Quasi-dense Matching Wide Baseline Images Mserdog Affine Propagation Adaptive Support Weight
Abstract三维重建一直是计算机视觉的一个重要研究内容。近年来,三维重建在虚拟现实、数字娱乐、三维电子地图、医学成像等领域得到了广泛应用。一般意义下的三维重建主要包括图像匹配、摄像机标定、三维结构恢复三个步骤。而图像匹配是这三个步骤中最关键、最困难的一个问题。由于建筑物图像本身存在纹理单一而且重复的特点,这又给图像匹配以及图像的准稠密扩散带来了不同程度的困难。本文基于室外建筑物场景,围绕图像特征匹配和准稠密扩散时遇到的问题展开研究,主要工作包括以下几个方面: 1.针对三维重建需要比较稠密的匹配以提高重建效果的要求,深入讨论了图像准稠密匹配的算法框架,并分别对短基线图像和宽基线图像的准稠密匹配,分析了两者使用的不同特征的提取算法,以及所运用的不同扩散技术和策略,如不同的对应邻域窗口的选择等。对于具有单一且重复纹理的宽基线建筑物场景图像的准稠密匹配,通过实验结果分析现有的准稠密匹配算法存在的一些问题和亟待改进的地方。 2.针对建筑物场景宽基线图像存在纹理单一且重复的特点,提出了一种适合该类图像的准稠密匹配算法。该算法采用新的特征区域提取算法,在高斯差分空间提取最大稳定极值区域(MSERDoG),提高了可靠的初始种子匹配点数量;同时,针对纹理重复的特点,采用自适应支持加权(ASW)作为匹配度量准则,给匹配点打分;针对宽基线图像的局部区域透视畸变严重的特点,采用仿射传递的方法,不断更新新的匹配点的仿射矩阵和对应邻域窗口,同时结合ASW匹配度量准则,找到精确的匹配对应点,消除了匹配多义性问题,并使得边界上的匹配更准确。 3.针对准稠密匹配中常见的问题,如边缘匹配问题,匹配点的分布问题、扩散过程的控制问题,以及针对三幅及以上图像之间的准稠密匹配需求,结合建筑物场景特点,给出了相关思考和相应的解决方案。
Other AbstractThree dimension(3D) reconstruction is an important research field in computer vision, which is widely used in virtual reality, digital entertainment, 3D electronic map, medical imaging, etc. Generally,the process of 3D reconstruction includes three steps: image matching, camera calibration, and recovery of 3D surface structure. Among these steps, image matching is the most important and critical problem. For outdoor building scenes, the textures are generally repetitive and locally insufficient, this brings difficulties to image matching and quasi-dense matching. This thesis focuses on how to resolve these difficulties and the main contributions include: 1.At least quasi-dense matching is required for 3D reconstruction in order to have better 3D visualization. We discuss the main frames of quasi-dense matching algorithms. In particular, we analyze a previous quasi-dense narrow baseline matching algorithm and a previous quasi-dense wide baseline matching algorithm, then we give their performance differences including different feature detectors, different propagation strategies such as corresponding neighborhood window selection. 2.A novel quasi-dense matching algorithm for building scenes with repetitive and locally insufficient textures is proposed. A new region detector MSERDoG(maximally stable extremal regions on difference of gaussian space) is used to obtain more initial seed matches, then affine propagation with ASW(adaptive support-weight) score is used to eliminate match ambiguity. In addition, matches on borders become more precise. 3.We discuss some classical problems in existing quasi-dense matching algorithms, like edge matching, adjustment of propagated matches, controllability of the propagation process, propagations from two above images. Base on building scenes, we provide some thoughts and possible solutions for these problems.
Other Identifier200728014629049
Document Type学位论文
Recommended Citation
GB/T 7714
陈占军. 建筑物场景宽基线图像的匹配扩散研究[D]. 中国科学院自动化研究所. 中国科学院研究生院,2010.
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